## Talking points

**for phone interview with Lucy**

Finding warning signals in the wild

- Gold standard – experimental group, control group
- Real world: We have no control group.

*Would we know a warning signal when we saw it?*

From statistics to models: We fit a generalized models of the processes that *are* and *aren’t* approaching a tipping point, and compare the models. The modeling approach is also more sensitive than the simple statistics.

In another recent paper we consider the alternative ground-truth approach – historical data. There’s a problem in using historical data though, which is a bit subtle. It has tricked juries, confounded medical doctors, and confused scientists. We call it the Prosecutor’s fallacy (Thompson & Schumann 87). We need the probability the “accused pattern” is “innocent” of heading for a critical transition, given the data. We calculate instead the probability of data, given that pattern is innocent, is small. By itself, that is not enough to “convict” our data of being a warning signal.

- Critical transitions in networks vs early warning signals – more must be done to map the connections.
- Be careful not to mistate generality – we know systems can collapse without transition, and warning signals can be seen without transitions